Noisy OR CPD

Initializes the NoisyORCPD class.

The NoisyOR CPD is a special case of TabularCPD for binary variables where a given variable can be activated by each of the parent variables with a specified probability. This activation probability is defined in the prob_values argument.

param variable:

The variable for which the CPD is to be defined.

type variable:

str

param prob_values:

A list of probabilities values for each evidence variable to activate variable.

type prob_values:

iterable

param evidence:

List of evidence variables, i.e., conditional variables.

type evidence:

list

Examples

>>> from pgmpy.factors.discrete import NoisyORCPD
>>> cpd = NoisyORCPD(variable='Y', prob_values=[0.3, 0.5], evidence=['X1', 'X2'])
# Defining a model containing NoisyORCPD
>>> from pgmpy.models import BayesianNetwork
>>> model = BayesianNetwork(['A', 'B'])
# With nodes with no parents, we can not define a NoisyORCPD.
>>> cpd_a = TabularCPD('A', 2, [[0.2], [0.8]], state_names={'A': ['True', 'False']})
>>> cpd_b = NoisyORCPD('B', [0.8], evidence=['A'])
>>> model.add_cpds(cpd_a, cpd_b)